Automaton

It’s not particularly difficult to make a robot that looks like an animal. It’s much harder to make a robot that behaves like an animal. At EPFL, a group led by Prof. Auke Jan Ijspeert has been working on swimming robots for over a decade, using the salamander as a model, and Pleurobot looks like the most accurate—and eerily lifelike—yet.

NASA’s Super Ball Bot has to be one of the most bizarrely and effectively innovative robot designs we’ve ever come across. It’s a tensegrity structure, nothing more (and nothing less) than a bunch of rods connected by a bunch of cables. It’s almost certainly not what you picture when you think of a robot, much less a robot that’s intended to head into space. At NASA Ames Research Center, they’ve been working on this project through NASA’s Innovative Advanced Concepts (NIAC) program, and we have an update for you about what they’ve been up to.

It’s going to be a long, long time before robots are sophisticated enough that we should worry about them taking over from humans. Having said that, there are things that are simple enough for artificial intelligence systems to learn to solve faster and more effectively than humans can. Like video games. When it comes to video games, humans really are doomed, and you can watch it happen right now.

Sony stopped making the Aibo robot dog in 2006. In robot years, that’s ages ago. Still, many robot enthusiasts would agree that these little robotic pets remain one of the most sophisticated consumer robot toys that you can ever hope to own. In fact, after the first Aibo was released in 1999, Sony worked very hard to improve the robot with each generation.

While consumer robots ultimately weren’t profitable for Sony, the Aibo is now an icon (there’s one at New York’s Museum of Modern Art), and the company did a good job of supporting Aibo owners with accessible software and repairs. But that’s all over now. According to a Wall Street Journal story, Sony is officially discontinuing Aibo maintenance services, citing lack of available spare parts.

It’s a good time to be in the market for a mobile manipulator. After recent announcements by both PAL Robotics and Fetch Robotics, Spanish company Robotnik has stepped up with their take on an affordable robot that can move around and actually, you know, do things for you: RB-1.

Amp Holdings is a company that’s making a hybrid electric delivery truck that costs delivery companies 30 cents per mile to operate, as opposed to the dollar per mile that diesel trucks cost. That sounds like it’s a thing that’s worth buying all by itself, but Amp also wants to integrate a delivery drone (called a HorseFly) into each truck to make short deliveries semi-autonomously.

Aldebaran Robotics has just announced that its founder and CEO Bruno Maisonnier is stepping down. We had heard rumors of leadership changes several weeks ago but now the Paris-based company has officially confirmed that SoftBank, which had acquired a majority stake in Aldebaran, will purchase all of the shares held by Maisonnier and appoint a new CEO.

How on earth do you make an algorithm exciting and visual? As journalists, we struggle with this all the time. MIT students, to the surprise of nobody, are way cleverer than we are, and they’ve developed a garden full of robotic flowers that can be programmed to physically illustrate the effects of algorithms on a set of data by opening, closing, and changing colors.

Watch them do their flowery thing, and then watch all the rest of Video Friday, because… uh, because it’s Video Friday. Yeah, that.

Artificial intelligence has gone through some dismal periods, which those in the field gloomily refer to as “AI winters.” This is not one of those times; in fact, AI is so hot right now that tech giants like Google, Facebook, Apple, Baidu, and Microsoft are battling for the leading minds in the field. The current excitement about AI stems, in great part, from groundbreaking advances involving what are known as “convolutional neural networks.” This machine learning technique promises dramatic improvements in things like computer vision, speech recognition, and natural language processing. You probably have heard of it by its more layperson-friendly name: “Deep Learning.”

Few people have been more closely associated with Deep Learning than Yann LeCun, 54. Working as a Bell Labs researcher during the late 1980s, LeCun developed the convolutional network technique and showed how it could be used to significantly improve handwriting recognition; many of the checks written in the United States are now processed with his approach. Between the mid-1990s and the late 2000s, when neural networks had fallen out of favor, LeCun was one of a handful of scientists who persevered with them. He became a professor at New York University in 2003, and has since spearheaded many other Deep Learning advances.

More recently, Deep Learning and its related fields grew to become one of the most active areas in computer research. Which is one reason that at the end of 2013, LeCun was appointed head of the newly-created Artificial Intelligence Research Lab at Facebook, though he continues with his NYU duties.

LeCun was born in France, and retains from his native country a sense of the importance of the role of the “public intellectual.” He writes and speaks frequently in his technical areas, of course, but is also not afraid to opine outside his field, including about current events.

IEEE Spectrum contributor Lee Gomes spoke with LeCun at his Facebook office in New York City. The following has been edited and condensed for clarity.

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